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We consider the problem of discovering causal relations from independenceconstraints selection bias in addition to confounding is present. While theseminal FCI algorithm is sound and complete in this setup, no criterion for thecausal interpretation of its output under selection bias is presently known. Wefocus instead on local patterns of independence relations, where we find nosound method for only three variable that can include background knowledge.Y-Structure patterns are shown to be sound in predicting causal relations fromdata under selection bias, where cycles may be present. We introduce afinite-sample scoring rule for Y-Structures that is shown to successfullypredict causal relations in simulation experiments that include selectionmechanisms. On real-world microarray data, we show that a Y-Structure variantperforms well across different datasets, potentially circumventing spuriouscorrelations due to selection bias.

Local Constraint-Based Causal Discovery under Selection Bias